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Stock Market Price Prediction Using SAP Predictive Service

  • Sanjana Devi
  • Virrat DevaserEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)

Abstract

Stock market price prediction is one of the most active areas of research among analysts from last few decades. Stock market price prediction is nothing but a process of trying to find the stock cost price for next trading day or next few days. A good prediction strategy may help to yield more profit. Different techniques and instruments are utilized to forecast the stock market price like artificial neural system, fuzzy logic, machine learning, Support Vector Machine, ARIMA model, R programming. Different algorithms are utilized to execute all these methods more precisely like Naïve Bayes, K-means, genetic Algorithms and Data mining algorithms and so on. The main intention is to increase the accuracy of forecast the stock market. Here, we are going to create a model with the help of Amazon web services, SAP Cloud Appliance Library. The Model is integrated with cloud to manage the dataset easily. SAP predictive services help to predict the future outcome in better way. Beauty of this model is it can handle large amount of data very easily. Like if user have historical data in the form of big data, it can not only easily managed in cloud environment but analyzing those data in very short time span is also possible with the help of SAP, because SAP is in-memory database.

Keywords

Data forecasting Stock market price prediction Cloud appliance library Amazon web services SAP predictive services 

References

  1. 1.
    Chen, D., Bin, F., Chen, C.: The Impacts of Political Events on Foreign Institutional Investors and Stock Returns : Emerging Market Evidence from Taiwan, vol. 10, no. 2 (2005)Google Scholar
  2. 2.
    Van Horne, J.C., Parker, G.G.C.: Theory : An Empirical Test. (1967)Google Scholar
  3. 3.
    Shankar, R.L.: Mispricing in single stock futures: empirical examination of Indian markets Mispricing in single stock futures: empirical examination of Indian markets, pp. 1–36 (2015)Google Scholar
  4. 4.
    Dupernex, S.: Why might share prices follow a random walk? Stud. Econ. Rev. 21, 167–179 (2007)Google Scholar
  5. 5.
    Hiemstra, Y.: A stock market forecasting support system based on fuzzy logic. In: 1994 Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences, vol. 3, pp. 281–287 (1994)Google Scholar
  6. 6.
    Setnes, M., Van Drempt, J.H.: Fuzzy modeling in stock-market analysis, pp. 250–258 (1999) Google Scholar
  7. 7.
    Andrade de Oliveira, F., Enrique Zarate, L., de Azevedo Reis, M., Neri Nobre, C.: The use of artificial neural networks in the analysis and prediction of stock prices. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2151–2155 (2011)Google Scholar
  8. 8.
    Iuhasz, G., Tirea, M., Negru, V.: Neural network predictions of stock price fluctuations. In: Proceedings-14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2012, pp. 505–512 (2012)Google Scholar
  9. 9.
    Olatunji, S.O., Al-Ahmadi, M.S., Elshafee, M., Fallatah, Y A.: Saudi Arabia stock prices forecasting using artificial neural networks. In: Fourth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2011), pp. 81–86 (2011)Google Scholar
  10. 10.
    Sood, P.K., Devaser, V.: Stock price prediction for a sectorial leader in NSE using neural network. Int. J. Appl. Eng. Res. 10(55), 2067–2074 (2015)Google Scholar
  11. 11.
    Nair, B.B., Dharini, N.M., Mohandas, V.P.: A stock market trend prediction system using a hybrid decision tree-neuro-fuzzy system. In: Proceedings- 2nd International Conference on Advances in Recent Technologies in Communication and Computing ARTCom 2010, pp. 381–385 (2010)Google Scholar
  12. 12.
    Lin, Y., Guo, H., Hu, J.: An SVM-based approach for stock market trend prediction. In: Proceedings of the International Joint Conference on Neural Networks (2013)Google Scholar
  13. 13.
    Basu, A., Watters, C., Shepherd, M.: Support vector machines for text categorization, pp. 1–7 (2002)Google Scholar
  14. 14.
    Milosevic, N.: Equity forecast: predicting long term stock price movement using machine learning. J. Econ. Libr. 3(2), 8 (2016)Google Scholar
  15. 15.
    Ryota, K., Tomoharu, N.: Stock market prediction based on interrelated time series data. In: 2012 IEEE Symposium on Computers & Informatics, Isc. 2012, pp. 17–21 (2012)Google Scholar
  16. 16.
    Shubhrata, M.D., Kaveri, D., Pranit, T., Bhavana, S.: Stock Market Prediction and Analysis Using Naïve Bayes. Int. J. Recent Innov. Trends Comput. Commun. 4(11), 121–124 (2016)Google Scholar
  17. 17.
    Attigeri, G.V., Manohara Pai, M.M., Pai, R.M., Nayak, A.: Stock market prediction: a big data approach. In: 35th IEEE Region 10 Conference TENCON 2015, January, 2016Google Scholar
  18. 18.
    Thomas, M.M.: A review paper on BIG Data. Int. Res. J. Eng. Technol. 4(4), 2395–56 (2015)Google Scholar
  19. 19.
    Kelley, K., Lai, K., Wu, P.-J.: Using R for data analysis: a best practice for research. In: Best Practices Quantitative Methods, pp. 535–572 (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringLovely Professional UniversityPhagwaraIndia

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